chore(anomaly_detection) Handle timeseries with very low variance

codecov.yml

@@ -16,6 +16,9 @@ coverage:
  trend_detection:
  paths:
  - 'src/seer/trend_detection/'
+ anomaly_detection:
+ paths:
+ - 'src/seer/anomaly_detection/'
  app:
  paths:
  - 'src/seer/app.py'

src/seer/anomaly_detection/anomaly_detection.py

@@ -26,6 +26,7 @@
 )
 from seer.dependency_injection import inject, injected
 from seer.exceptions import ClientError, ServerError
+from seer.tags import AnomalyDetectionModes, AnomalyDetectionTags
 
 anomaly_detection_module.enable()
 logger = logging.getLogger(__name__)
@@ -226,16 +227,16 @@ def detect_anomalies(self, request: DetectAnomaliesRequest) -> DetectAnomaliesRe
  Anomaly detection request that has either a complete time series or an alert reference.
  """
  if isinstance(request.context, AlertInSeer):
- mode = "streaming.alert"
+ mode = AnomalyDetectionModes.STREAMING_ALERT
  elif isinstance(request.context, TimeSeriesWithHistory):
- mode = "streaming.ts_with_history"
+ mode = AnomalyDetectionModes.STREAMING_TS_WITH_HISTORY
  else:
- mode = "batch.ts_full"
+ mode = AnomalyDetectionModes.BATCH_TS_FULL
 
- sentry_sdk.set_tag("ad_mode", mode)
+ sentry_sdk.set_tag(AnomalyDetectionTags.MODE, mode)
 
  if isinstance(request.context, AlertInSeer):
- sentry_sdk.set_tag("alert_id", request.context.id)
+ sentry_sdk.set_tag(AnomalyDetectionTags.ALERT_ID, request.context.id)
  ts, anomalies = self._online_detect(request.context, request.config)
  elif isinstance(request.context, TimeSeriesWithHistory):
  ts, anomalies = self._combo_detect(request.context, request.config)
@@ -255,6 +256,7 @@ def store_data(
  request: StoreDataRequest
  Alert information along with underlying time series data
  """
+ sentry_sdk.set_tag(AnomalyDetectionTags.ALERT_ID, request.alert.id)
  # Ensure we have at least 7 days of data in the time series
  min_len = self._min_required_timesteps(request.config.time_period)
  if len(request.timeseries) < min_len:
@@ -302,5 +304,6 @@ def delete_alert_data(
  request: DeleteAlertDataRequest
  Alert to clear
  """
+ sentry_sdk.set_tag(AnomalyDetectionTags.ALERT_ID, request.alert.id)
  alert_data_accessor.delete_alert_data(external_alert_id=request.alert.id)
  return DeleteAlertDataResponse(success=True)

src/seer/anomaly_detection/detectors/anomaly_detectors.py

@@ -83,7 +83,6 @@ def _compute_matrix_profile(
  """
  ts_values = timeseries.values # np.array([np.float64(point.value) for point in timeseries])
  window_size = ws_selector.optimal_window_size(ts_values)
- logger.debug(f"window_size: {window_size}")
  if window_size <= 0:
  # TODO: Add sentry logging of this error
  raise ServerError("Invalid window size")

src/seer/anomaly_detection/detectors/mp_scorers.py

@@ -3,9 +3,11 @@
 
 import numpy as np
 import numpy.typing as npt
+import sentry_sdk
 from pydantic import BaseModel
 
 from seer.anomaly_detection.models import AnomalyFlags, Directions, Sensitivities
+from seer.tags import AnomalyDetectionTags
 
 logger = logging.getLogger(__name__)
 
@@ -41,6 +43,20 @@ def stream_score(
 
 
 class MPIRQScorer(MPScorer):
+ def _flag_if_low_variance(
+ self, ts_value: float, variance: float, mean: float
+ ) -> AnomalyFlags | None:
+ if variance <= 0.0001:
+ logger.info("Timeseries has very low variance")
+ sentry_sdk.set_tag(AnomalyDetectionTags.LOW_VARIANCE_TS, 1)
+ # if current value is significantly higher or lower than the mean then mark it as high anomaly else mark it as no anomaly
+ if abs(ts_value) >= 2 * abs(mean):
+ return "anomaly_higher_confidence"
+ else:
+ return "none"
+ # If there is enough variance in the data then we do not need to compare to mean
+ sentry_sdk.set_tag(AnomalyDetectionTags.LOW_VARIANCE_TS, 0)
+ return None
 
  def batch_score(
  self,
@@ -86,13 +102,17 @@ def batch_score(
  # Compute score and anomaly flags
  scores = []
  flags = []
+ ts_variance = ts.var()
+ ts_mean = ts.mean()
  for i, val in enumerate(mp_dist):
  scores.append(0.0 if np.isnan(val) or np.isinf(val) else val - threshold_upper)
- flag = self._to_flag(val, threshold_lower, threshold_upper)
- if i > 2 * window_size:
- flag = self._adjust_flag_for_vicinity(
- flag=flag, ts_value=ts[i], context=ts[i - 2 * window_size : i - 1]
- )
+ flag = self._flag_if_low_variance(ts_value=ts[i], variance=ts_variance, mean=ts_mean)
+ if flag is None:
+ flag = self._to_flag(val, threshold_lower, threshold_upper)
+ if i > 2 * window_size:
+ flag = self._adjust_flag_for_vicinity(
+ flag=flag, ts_value=ts[i], context=ts[i - 2 * window_size : i - 1]
+ )
  flags.append(flag)
 
  return scores, flags
@@ -130,23 +150,30 @@ def stream_score(
  * "anomaly_lower_confidence" - indicating anomaly but only with a lower threshold
  * "anomaly_higher_confidence" - indicating anomaly with a higher threshold
  """
- # Stumpy returns inf for the first timeseries[0:window_size - 2] entries. We just need to ignore those before scoring.
- mp_dist_baseline_finite = mp_dist_baseline[np.isfinite(mp_dist_baseline)]
-
- # Compute the quantiles for two different threshold levels
- [Q1, Q3] = np.quantile(mp_dist_baseline_finite, [0.25, 0.75])
- IQR_L = Q3 - Q1
- threshold_lower = Q3 + (1.5 * IQR_L)
+ context = ts_baseline[-2 * window_size :]
+ flag = self._flag_if_low_variance(
+ ts_value=ts_value, variance=context.var(), mean=context.mean()
+ )
+ score = 0.0
+ if flag is None:
+ # Stumpy returns inf for the first timeseries[0:window_size - 2] entries. We just need to ignore those before scoring.
+ mp_dist_baseline_finite = mp_dist_baseline[np.isfinite(mp_dist_baseline)]
+
+ # Compute the quantiles for two different threshold levels
+ [Q1, Q3] = np.quantile(mp_dist_baseline_finite, [0.25, 0.75])
+ IQR_L = Q3 - Q1
+ threshold_lower = Q3 + (1.5 * IQR_L)
+
+ [Q1, Q3] = np.quantile(mp_dist_baseline_finite, [0.15, 0.85])
+ IQR_L = Q3 - Q1
+ threshold_upper = Q3 + (1.5 * IQR_L)
+
+ # Compute score and anomaly flags
+ score = 0.0 if np.isnan(mp_dist) or np.isinf(mp_dist) else mp_dist - threshold_upper
+ flag = self._to_flag(mp_dist, threshold_lower, threshold_upper)
+ # anomaly identified. apply logic to check for peak and trough
+ flag = self._adjust_flag_for_vicinity(ts_value, flag, ts_baseline[-2 * window_size :])
 
- [Q1, Q3] = np.quantile(mp_dist_baseline_finite, [0.15, 0.85])
- IQR_L = Q3 - Q1
- threshold_upper = Q3 + (1.5 * IQR_L)
-
- # Compute score and anomaly flags
- score = 0.0 if np.isnan(mp_dist) or np.isinf(mp_dist) else mp_dist - threshold_upper
- flag = self._to_flag(mp_dist, threshold_lower, threshold_upper)
- # anomaly identified. apply logic to check for peak and trough
- flag = self._adjust_flag_for_vicinity(ts_value, flag, ts_baseline[-2 * window_size :])
  return [score], [flag]
 
  def _to_flag(self, mp_dist: float, threshold_lower: float, threshold_upper: float):

src/seer/anomaly_detection/detectors/normalizers.py

@@ -18,4 +18,9 @@ def normalize(self, array: npt.NDArray) -> npt.NDArray:
 class MinMaxNormalizer(Normalizer):
  def normalize(self, array: npt.NDArray) -> npt.NDArray:
  """Applies min-max normalization to input array"""
+ if array.var() == 0:
+ if array[0] == 0:
+ return array
+ else:
+ return np.full_like(array, 1.0)
  return (array - np.min(array)) / (np.max(array) - np.min(array))

src/seer/anomaly_detection/detectors/window_size_selectors.py

@@ -3,9 +3,11 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
+import sentry_sdk
 from pydantic import BaseModel, Field
 
 from seer.anomaly_detection.detectors.normalizers import MinMaxNormalizer, Normalizer
+from seer.tags import AnomalyDetectionTags
 
 
 class WindowSizeSelector(BaseModel, abc.ABC):
@@ -84,9 +86,13 @@ def optimal_window_size(self, time_series: npt.NDArray[np.float64]) -> int:
  The time series as a seaquence of float values.
 
  """
+ if time_series.var() == 0:
+ sentry_sdk.set_tag(AnomalyDetectionTags.WINDOW_SEARCH_FAILED, 1)
+ sentry_sdk.set_tag(AnomalyDetectionTags.LOW_VARIANCE_TS, 1)
+ return 3
  time_series = self.normalizer.normalize(time_series)
- ts_mean = np.mean(time_series)
- ts_std = np.std(time_series)
+ ts_mean = time_series.mean()
+ ts_std = time_series.std()
  ts_min_max = np.max(time_series) - np.min(time_series)
 
  lbound = self.lbound
@@ -119,7 +125,9 @@ def optimal_window_size(self, time_series: npt.NDArray[np.float64]) -> int:
  exp += 1
 
  if not found_window:
- raise Exception("Search for optimal window failed.")
+ # raise Exception("Search for optimal window failed.")
+ sentry_sdk.set_tag(AnomalyDetectionTags.WINDOW_SEARCH_FAILED, 1)
+ return 3
 
  lbound = max(lbound, 2 ** (exp - 1))
 
@@ -136,5 +144,9 @@ def optimal_window_size(self, time_series: npt.NDArray[np.float64]) -> int:
  ubound = window_size - 1
  else:
  break
-
- return 2 * lbound
+ window_size = 2 * lbound
+ if window_size >= len(time_series):
+ sentry_sdk.set_tag(AnomalyDetectionTags.WINDOW_SEARCH_FAILED, 1)
+ return 3
+ sentry_sdk.set_tag(AnomalyDetectionTags.WINDOW_SEARCH_FAILED, 0)
+ return window_size

src/seer/tags.py

@@ -0,0 +1,14 @@
+from enum import StrEnum
+
+
+class AnomalyDetectionTags(StrEnum):
+ ALERT_ID = "alert_id"
+ MODE = "mode"
+ LOW_VARIANCE_TS = "low_variance_ts"
+ WINDOW_SEARCH_FAILED = "window_search_failed"
+
+
+class AnomalyDetectionModes(StrEnum):
+ STREAMING_ALERT = "streaming.alert"
+ STREAMING_TS_WITH_HISTORY = "streaming.ts_with_history"
+ BATCH_TS_FULL = "batch.ts_full"